Global Meat Production Analysis: Trends & Statistical Precision

Understanding 60+ years of regional production patterns through time trends and recent statistical analysis

MakeoverMonday
Data Visualization
R Programming
2025
A comprehensive analysis of global meat production from 1961-2023, combining historical trend visualization with statistical precision analysis. This MakeoverMonday project transforms FAO data into a dual-panel visualization that reveals Asia’s dramatic rise in meat production while providing statistical confidence intervals for current production levels across six regions and four major meat types.
Author

Steven Ponce

Published

August 26, 2025

Original

The original visualization Global meat production, 1961 to 2023 comes from Food and Agriculture Organization of the United Nations (2025)

Original visualization

Makeover

Figure 1: Two-panel chart analyzing global meat production. The top panel displays regional trends from 1961 to 2023, featuring line graphs for six regions (Asia, North America, Europe, South America, Africa, and Oceania). This reveals Asia’s dramatic growth in pig and poultry production, reaching over 60 million tonnes, while the other regions remain relatively flat. The bottom panel displays 2010-2023 statistical confidence intervals using dot plots with error bars across four meat types (Poultry, Pig, Beef & Buffalo, and Sheep & Goat), confirming Asia’s dominance in most categories, with North America leading in beef production. Color coding is consistent throughout: brown for beef, bright pink for pig, orange for poultry, and green for sheep.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse,  # Easily Install and Load the 'Tidyverse'
    ggtext,     # Improved Text Rendering Support for 'ggplot2'
    showtext,   # Using Fonts More Easily in R Graphs
    scales,     # Scale Functions for Visualization
    glue,       # Interpreted String Literals
    patchwork,  # The Composer of Plots
    tidytext    # Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools 
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 10,
    height = 10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|
meat_production_raw <- readxl::read_excel(
  here::here('data/MakeoverMonday/2025/Global meat production by livestock type.xlsx')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(meat_production_raw)
skimr::skim_without_charts(meat_production_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

meat_clean <- meat_production_raw |>
  filter(!is.na(entity), !entity %in% c("World", "")) |>
  pivot_longer(
    cols = starts_with("meat_"),
    names_to = "meat_type_raw",
    values_to = "production_tonnes",
    values_drop_na = TRUE
  ) |>
  # Clean meat type names
  mutate(
    meat_type = case_when(
      str_detect(meat_type_raw, "beef") ~ "Beef & Buffalo",
      str_detect(meat_type_raw, "pig") ~ "Pig",
      str_detect(meat_type_raw, "poultry") ~ "Poultry",
      str_detect(meat_type_raw, "sheep") ~ "Sheep & Goat",
      str_detect(meat_type_raw, "game") ~ "Game",
      str_detect(meat_type_raw, "horse") ~ "Horse",
      str_detect(meat_type_raw, "camel") ~ "Camel",
      TRUE ~ "Other"
    )
  ) |>
  # Focus on major meat types for cleaner visualizations
  filter(meat_type %in% c("Beef & Buffalo", "Pig", "Poultry", "Sheep & Goat")) |>
  select(entity, code, year, meat_type, production_tonnes)

# Create regional groupings for continental analysis
regional_data <- meat_clean |>
  mutate(
    region = case_when(
      entity %in% c(
        "China", "India", "Japan", "South Korea", "Indonesia", "Thailand",
        "Philippines", "Vietnam", "Malaysia", "Bangladesh", "Pakistan"
      ) ~ "Asia",
      entity %in% c("United States", "Canada", "Mexico") ~ "North America",
      entity %in% c(
        "Brazil", "Argentina", "Chile", "Colombia", "Peru", "Uruguay",
        "Venezuela", "Ecuador", "Bolivia"
      ) ~ "South America",
      entity %in% c(
        "Germany", "France", "United Kingdom", "Italy", "Spain", "Poland",
        "Netherlands", "Russia", "Ukraine", "Turkey"
      ) ~ "Europe",
      entity %in% c(
        "Nigeria", "Ethiopia", "South Africa", "Kenya", "Ghana", "Morocco",
        "Algeria", "Egypt", "Tanzania", "Uganda"
      ) ~ "Africa",
      entity %in% c("Australia", "New Zealand") ~ "Oceania",
      TRUE ~ "Other"
    )
  ) |>
  filter(region != "Other")

# P1: Regional trends over time data ----
trends_data <- regional_data |>
  group_by(region, year, meat_type) |>
  summarise(total_production = sum(production_tonnes, na.rm = TRUE), .groups = "drop") |>
  # Calculate growth rates 
  group_by(region, meat_type) |>
  arrange(year) |>
  mutate(
    growth_from_start = (total_production / first(total_production) - 1) * 100
  ) |>
  ungroup() |>
  # Order regions by total 2023 production 
  group_by(region) |>
  mutate(region_total_2023 = sum(total_production[year == 2023], na.rm = TRUE)) |>
  ungroup() |>
  mutate(region = fct_reorder(region, desc(region_total_2023)))

# P2: Statistical precision data ----
precision_data <- regional_data |>
  filter(year >= 2010) |>
  group_by(region, meat_type) |>
  summarise(
    n_years = n(),
    mean_production = mean(production_tonnes, na.rm = TRUE),
    sd_production = sd(production_tonnes, na.rm = TRUE),
    se_production = sd_production / sqrt(n_years),
    .groups = "drop"
  ) |>
  mutate(
    # 95% confidence intervals
    lower_ci = pmax(0, mean_production - 1.96 * se_production),
    upper_ci = mean_production + 1.96 * se_production
  ) |>
  # Order meat types by total global production 
  group_by(meat_type) |>
  mutate(meat_total = sum(mean_production)) |>
  ungroup() |>
  mutate(meat_type_ordered = fct_reorder(meat_type, desc(meat_total))) |>
  # Use tidytext::reorder_within for proper within-facet ordering
  mutate(region_ordered = reorder_within(region, mean_production, meat_type_ordered))
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  "Beef & Buffalo" = "#8B4513",  
  "Pig" = "#E91E63",            
  "Poultry" = "#FF9800",        
  "Sheep & Goat" = "#2E7D32"    
))   

### |-  titles and caption ----
title_text <- str_glue("Global Meat Production Analysis: Trends & Statistical Precision")

subtitle_text <- str_glue(
  "Understanding 60+ years of regional production patterns through time trends and recent statistical analysis"
)

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("FAO via Our World in Data")
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.title = element_text(
      size = rel(1.4), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "top",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),
    legend.title = element_text(face = "bold"),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.ticks.length = unit(0.2, "cm"),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

### |- P1  Regional trends over time chart ----
p1 <- trends_data |>
  ggplot(aes(x = year, y = total_production, color = meat_type)) +
  
  # Geoms
  geom_line(size = 1.1, alpha = 0.9) +
  geom_smooth(method = "loess", se = FALSE, size = 0.8, alpha = 0.6, span = 0.3) +
  # Scales
  scale_color_manual(values = colors$palette, name = "Meat Type") +
  scale_y_continuous(
    labels = label_number(scale = 1e-6, suffix = "M", accuracy = 0.1),
    expand = expansion(mult = c(0.02, 0.05))
  ) +
  scale_x_continuous(
    breaks = seq(1970, 2020, 25),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  # Labs
  labs(
    title = "Regional Meat Production Evolution (1961-2023)",
    subtitle = str_glue(
      "Six decades of evolution (1961-2023) revealing long-term growth patterns and regional shifts<br>",
      "Fixed y-scale enables cross-regional comparison"
    ),
    x = "Year",
    y = "Production (Million Tonnes)"
  ) +
  # Facets
  facet_wrap(~region, scales = "fixed", ncol = 3) +
  # Theme
  theme(
    strip.text = element_text(face = "bold", size = rel(0.9), color = "white", family = fonts$text),
    strip.background = element_rect(fill = "#34495e", color = NA),
    )

### |- P2  Statistical precision chart----
p2 <- precision_data |>
  ggplot(aes(x = mean_production, y = region_ordered, color = meat_type)) +
  # Geoms
  geom_errorbarh(
    aes(xmin = lower_ci, xmax = upper_ci),
    height = 0.25, alpha = 0.7, linewidth = 0.5
  ) +
  geom_point(size = 2, alpha = 0.9) +
  # Scales
  scale_color_manual(values = colors$palette, guide = "none") +
  scale_x_continuous(
    labels = label_number(scale = 1e-6, suffix = "M", accuracy = 0.1),
    expand = expansion(mult = c(0.05, 0.1))
  ) +
  scale_y_reordered() +
  # Labs
  labs(
    title = "Regional Production Averages with Statistical Confidence (2010-2023)",
    subtitle = str_glue(
      "Statistical precision analysis of current era (2010-2023) with 95% confidence intervals<br>",
      "Recent period provides reliable uncertainty measurements"
    ),
    x = "Average Annual Production (Million Tonnes)",
    y = "Region"
  ) +
  # Facets
  facet_wrap(~meat_type_ordered, scales = "free_y", ncol = 2) +
  # Theme
  theme(
    strip.text = element_text(face = "bold", size = rel(0.9), color = "white", family = fonts$text),
    strip.background = element_rect(fill = "#34495e", color = NA)
    )

### |-  combined plot ----
combined_plots <- p1 / p2 +
  plot_layout(heights = c(1, 1)) 

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.7),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ), 
      plot.subtitle = element_markdown(
        size = rel(0.95),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 0.9,
        margin = margin(t = 5, b = 0)
      ),
      plot.caption = element_markdown(
        size = rel(0.65),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  height = 12
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      tidytext_0.4.2  patchwork_1.3.0 glue_1.8.0     
 [5] scales_1.3.0    showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      janitor_2.2.0     
 [5] janeaustenr_1.0.0  digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4   
 [9] rsvg_2.6.1         tokenizers_0.3.0   magrittr_2.0.3     compiler_4.4.0    
[13] rlang_1.1.6        tools_4.4.0        utf8_1.2.4         yaml_2.3.10       
[17] knitr_1.49         skimr_2.1.5        labeling_0.4.3     htmlwidgets_1.6.4 
[21] curl_6.0.0         xml2_1.3.6         camcorder_0.1.0    repr_1.1.7        
[25] withr_3.0.2        grid_4.4.0         fansi_1.0.6        colorspace_2.1-1  
[29] cli_3.6.4          rmarkdown_2.29     generics_0.1.3     rstudioapi_0.17.1 
[33] tzdb_0.5.0         commonmark_1.9.2   readxl_1.4.3       splines_4.4.0     
[37] ggplotify_0.1.2    cellranger_1.1.0   base64enc_0.1-3    vctrs_0.6.5       
[41] yulab.utils_0.1.8  Matrix_1.7-0       jsonlite_1.8.9     gridGraphics_0.5-1
[45] hms_1.1.3          systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1   
[49] codetools_0.2-20   stringi_1.8.4      gtable_0.3.6       munsell_0.5.1     
[53] pillar_1.9.0       htmltools_0.5.8.1  R6_2.5.1           rprojroot_2.0.4   
[57] evaluate_1.0.1     lattice_0.22-6     markdown_1.13      SnowballC_0.7.1   
[61] gridtext_0.1.5     snakecase_0.11.1   renv_1.0.3         Rcpp_1.0.13-1     
[65] svglite_2.1.3      nlme_3.1-164       mgcv_1.9-1         xfun_0.49         
[69] fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

The complete code for this analysis is available in mm_2025_35.qmd.

For the full repository, click here.

10. References

  1. Data:
  1. Article
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